Smooth optimization approach for sparse covariance selection

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In this paper we first study a smooth optimization approach for solving a class of nonsmooth strictly concave maximization problems whose objective functions admit smooth convex minimization reformulations. In particular, we apply Nesterov's smooth optimization technique [Y. E. Nesterov, Dokl. Akad. Nauk SSSR, 269 (1983), pp. 543-547; Y. E. Nesterov, Math. Programming, 103 (2005), pp. 127-152] to their dual counterparts that are smooth convex problems. It is shown that the resulting approach has O(1/√∈) iteration complexity for finding an ∈-optimal solution to both primal and dual problems. We then discuss the application of this approach to sparse covariance selection that is approximately solved as an ℓ1-norm penalized maximum likelihood estimation problem, and also propose a variant of this approach which has substantially outperformed the latter one in our computational experiments. We finally compare the performance of these approaches with other first-order methods, namely, Nesterov's O(1/∈) smooth approximation scheme and blockcoordinate descent method studied in [A. d'Aspremont, O. Banerjee, and L. El Ghaoui, SIAM J. Matrix Anal. Appl., 30 (2008), pp. 56-66; J. Friedman, T. Hastie, and R. Tibshirani, Biostatistics, 9 (2008), pp. 432-441] for sparse covariance selection on a set of randomly generated instances. It shows that our smooth optimization approach substantially outperforms the first method above, and moreover, its variant substantially outperforms both methods above.

Original languageEnglish (US)
Pages (from-to)1807-1827
Number of pages21
JournalSIAM Journal on Optimization
Issue number4
StatePublished - 2008
Externally publishedYes


  • Nonsmooth strictly concave maximization
  • Smooth minimization
  • Sparse covariance selection


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